<html>

<head>
<meta http-equiv="Content-Language" content="en-ca">
<meta name="GENERATOR" content="Microsoft FrontPage 6.0">
<meta name="ProgId" content="FrontPage.Editor.Document">
<meta http-equiv="Content-Type" content="text/html; charset=windows-1252">
<title>New Page 1</title>
<style type="text/css">
.auto-style1 {
	text-align: center;
}
.auto-style2 {
	border-width: 0px;
}
.auto-style3 {
	background-color: #FFFF00;
}
</style>
</head>

<body>

<h1 align="center">Nonlinear Regression Overview</h1>

<p>Integration of nonlinear regression analysis into the LRE Analyzer provides 
effective solutions for two limitations that can compromise both the robustness 
and accuracy of LRE qPCR. </p>
<p>The first is determining fluorescence background (Fb), which is subtracted 
from the raw fluorescence readings that generates the fluorescence dataset 
needed for analysis. Historically, Fb is determined by averaging the 
fluorescence readings from the earliest cycles of a profile, before amplicon DNA 
becomes detectable, a region generally referred to as the baseline. </p>
<p>While this approach is generally effective, even relatively small errors in 
Fb determination can generate significant quantitative errors for LRE-based 
qPCR, a situation exacerbated by the propensity of artefacts to be generated 
within the first few cycles of a profile.</p>
<p>The second is referred to as baseline drifting, in which the baseline has a 
slope, either positive or negative. This too has long been known to compromise 
the quantitative accuracy of LRE-base qPCR, although until recently, it had been 
assumed to be rare and amplicon-specific, both of which have subsequently been 
proven to be wrong. </p>
<p>Nonlinear regression analysis, also commonly referred to as &quot;curve fitting&quot;, 
provides a potential solution to both of this issues, through its ability to 
provide estimates for Fb and Fb-slope. Nevertheless, an earlier study had shown 
that artefacts within the plateau phase greatly complicates effective 
application of nonlinear regression to qPCR (<a href="../lre_overview/lre_literature.html#SCF">Rutledge 
2004</a>). </p>
<p>A simple and surprisingly effective solution was to combine LRE analysis with 
nonlinear regression, in which the LRE window is used to define the upper limit 
of the cycles included in the nonlinear regression analysis. This allows cycles within the plateau phase to be excluded from the nonlinear 
regression analysis. Note that although values for Emax, Fmax and Fo are also 
generated by nonlinear regression, only Fb and baseline slope are used in the 
LRE analysis. </p>
<p>Further information about integrating nonlinear regression into the program 
is provided in the <a href="../editor_panel/profile_editor_window.html#NR Panel">
nonlinear regression panel</a> section.</p>
<p>Those interested in a more detailed description of how nonlinear regression 
was implemented can contact the author via RGRutledge@gmail.com.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p>&nbsp;</p>

</body>

</html>
